Domaine > Informatique.

Descriptif

Insect colonies, evolving species, economic communities, social networks are complex systems. Complex systems are collective entities, composed of many similar agents, that show emerging behaviour. Though the interactions between agents are too complex to be described, their collective behaviour often obeys much simpler rules. The objective of this course is to describe some of the laws that control emergent behaviour and allow to predict it. The course will address conceptual issues, at the frontier between biology and engineering. Each afternoon consists in a lab work session in which students will get an intuitive and concrete approach to phenomena such as genetic algorithms, ant-based problem solving, collective decision, cultural emergence or sex ratio in social insects. An ant colony can find the shortest path in a complex environment; a species can solve complex adaptation problems; economic agents may spontaneously reach a locally optimal allocation of resources. Simple individual acts, in each case, produce non-trivial results at the collective level. These observations constitute a rich source of inspiration for innovative engineering solutions, such as optimization using genetic algorithms, or message routing in telecom networks. The emergent behaviour of complex collective systems often goes against intuition. Its dynamics can be described through non-linear models that predict sudden transitions. Emergence is best apparent during those transitions. Its study consists in accounting for the appearance of collective patterns when individual, generally simple, behaviours are given as input. The main techniques studied in this module are: - Genetic algorithms, in which a virtual population evolves and collectively adapts to a particular problem or to a new environment. - Swarm intelligence, as a model of natural phenomena and as a class of collective algorithms. They are used to address problems in which adaptability and robustness are essential. - Emergence of phenomena like morphogenesis, cooperation, segregation through symmetry breaking, and emergence in social networks. We show how these different models can be applied to concrete problems, such as message routing in communication networks, optimal resource allocation or the emergence of communication. The notion of emergence is formally defined, as well as concepts like punctuated equilibria, scale invariance, implicit parallelism and autocatalytic phenomena.

nombre d'heure en présentiel

effectifs minimal / maximal

10/12

Format des notes

Numérique sur 20

Littérale/grade européen

Pour les étudiants du diplôme Diplôme d'ingénieur

La note obtenue rentre dans le calcul de votre GPA.

Programme détaillé

An ant colony can find the shortest path in a complex environment; a species can solve complex adaptation problems; economic agents may spontaneously reach a locally optimal allocation of resources. Simple individual acts, in each case, produce non-trivial results at the collective level.

These observations constitute a rich source of inspiration for innovative engineering solutions, such as optimization using genetic algorithms, or message routing in telecom networks. The emergent behaviour of complex collective systems often goes against intuition. Its dynamics can be described through non-linear models that predict sudden transitions. Emergence is best apparent during those transitions. Its study consists in accounting for the appearance of collective patterns when individual, generally simple, behaviours are given as input.

The main techniques studied in this module are: - Genetic algorithms, in which a virtual population evolves and collectively adapts to a particular problem or to a new environment. - Swarm intelligence, as a model of natural phenomena and as a class of collective algorithms. They are used to address problems in which adaptability and robustness are essential. - Emergence of phenomena like morphogenesis, cooperation, segregation through symmetry breaking, and emergence in social networks. We show how these different models can be applied to concrete problems, such as message routing in communication networks, optimal resource allocation or the emergence of communication. The notion of emergence is formally defined, as well as concepts like punctuated equilibria, scale invariance, implicit parallelism and autocatalytic phenomena.